This disclosure is related to the field of detection of hypocenters (origin time and position in the subsurface) from passive seismic signals. Passive seismic signals are those detected resulting from microseismic events occurring in the Earth's subsurface, whether the microseismic events are naturally occurring or induced by other activities. More specifically, the disclosure relates to methods for using semblance of corrected amplitudes of passively detected and recorded seismic signals to determine what in the signals are caused by actual microseismic events and to determine the hypocenters of such events.
Passive seismic signal detection and signal processing methods are widely used for microseismic monitoring of hydraulic fracturing. In such uses, large arrays of seismic sensors deployed at the Earth's surface, buried in shallow boreholes or installed in monitoring wells are used to map induced seismicity. The goals of microseismic data processing include event detection, estimation of hypocenter locations, determination of source mechanisms and magnitudes characterizing induced events. These results may then be used for creation of geomechanical models or simple computation of stimulated rock volume representing a response of the reservoir to stimulation. See, Neuhaus, C. W., Blair, K. Telker, C. and Ellison, M. (2013), Hydrocarbon Production and Microseismic Monitoring—Treatment Optimization in the Marcellus Shale, 75th EAGE Conference & Exhibition incorporating SPE EUROPEC 2013, SPE-164807-MS, and Hummel, N. and Shapiro, S. (2013). Nonlinear diffusion-based interpretation of induced microseismicity: A Barnett Shale hydraulic fracturing case study, Geophysics, 78(5), B211-B226. doi: 10.1190/geo2012-0242.1.
Because the exact origin time of a microseismic event is not known a priori, in passive seismic surveying seismic signals are acquired continuously for a selected time duration and search routines are implemented to detect events in the acquired signals. To do so one may use multichannel processing of large data sets where events are represented by compressional (P) and/or shear (S) wave arrivals in each seismic sensor signal record trace (a time indexed recording of seismic signal amplitude). However, P and S wave arrivals may not be detectable (e.g., by visual observation or threshold amplitude detection) due to a low signal-to-noise ratio (SNR) which makes event detection in unstacked trace gathers difficult. While generally background noise is higher for surface deployed seismic sensor arrays than for arrays deployed in one or more wellbores, lower amplitude arrivals in both surface and borehole arrays can usually compensated by stacking of signals from a large number of seismic sensors covering a wide range of offsets and azimuths, typically then processed with migration techniques (See, Duncan, P. and Eisner, L. (2010), Reservoir characterization using surface microseismic monitoring, Geophysics, 75(5), 75A139-75A146. doi: 10.1190/1.3467760). An object of microseismic monitoring techniques is to detect microseismic events, including events that are not readily detectable in unstacked trace gathers.
Migration-based microseismic event detection techniques usually rely on obtaining a high value of a trace sum stack along a moveout curve (a seismic sensor offset dependent time shift for each trace related to the seismic energy velocity distribution in the subsurface) computed from a hypothetical source position, thereby improving the SNR of unstacked traces. See, Duncan and Eisner, 2010, Chambers, K., Kendall, J.-M., Brandsberg-Dahl, S. and Rueda, J. (2010), Testing the ability of surface arrays to monitor microseismic activity, Geophysical Prospecting, 58: 821-830. doi: 10.1111/j.1365-2478.2010.00893.x, Gharti, H., Oye, V., Roth, M., and Kühn, D. (2010), Automated microearthquake location using envelope stacking and robust global optimization, Geophysics, 75(4), MA27-MA46. doi: 10.1190/1.3432784, and Bradford, I., Probert, T., Raymer, D., Ozbek, A., Primiero, P., Kragh, E., Drew, J. and Woerpel, C. (2013), Application of Coalescence Microseismic Mapping to Hydraulic Fracture Monitoring Conducted Using a Surface Array, 75th EAGE Conference & Exhibition incorporating SPE EUROPEC 2013. doi: 10.3997/2214-4609.20131028). However, typical microseismic events do not radiate seismic energy symmetrically as do controlled seismic sources such as dynamite explosions, seismic vibrators and seismic air guns deployed in water. The radiated energy and amplitude polarity of the energy from microseismic events or microearthquakes are strongly directional and have specific signatures due to the specific energy radiation patters of various microseismic source mechanisms. Recorded seismic signal amplitudes from a particular microseismic event may have different polarities and amplitudes at different seismic sensors that differ markedly from what would be anticipated assuming simple symmetrical geometrical spreading of seismic energy from the origin of any microseismic event. Hence, if one simply stacked both positive and negative polarity seismic signals with respect to position or offset one would obtain very low stacked signal amplitude values. The foregoing result may be overcome by stacking the absolute values of signal amplitudes, but at the cost of reducing the SNR of the stacked signal amplitudes. Zhebel, O. and Eisner, L. (2012), Simultaneous microseismic event localization and source mechanism determination, SEG Technical Program Expanded Abstracts 2012: pp. 1-5. doi: 10.1190/segam2012-1033.1 and Chambers, K., Clarke, J., Velasco, R. and Dando B. (2013), Surface Array Moment Tensor Microseismic Imaging, 75th EAGE Conference & Exhibition incorporating SPE EUROPEC 2013, doi: 10.3997/2214-4609.20130404 describe methods capable of simultaneously determining the origin location and source mechanism of microseismic events. The foregoing methods use a moment tensor inversion of P-wave (or S-wave) amplitudes taken along the moveout direction for every potential origin point in three dimensional (3D) subsurface space and then correct the polarity of detected signal amplitudes using the inverted moment tensor before stacking. Thus, the foregoing methods may obtain the highest stack value for the correct event origin location and source mechanism.
A challenge in using stacking is that only a few, or in extreme cases even one high amplitude noisy trace may result in high stack amplitudes indicating a spurious detection, the so called “false positive.” See, Thornton, M. and Eisner, L., “Uncertainty in surface microseismic monitoring, SEG Technical Program Expanded Abstracts, 2011: pp. 1524-1528. doi: 10.1190/1.3627492